// Covering
public SigmaNormalClassifier( State S, List<char> Actions, int ExpThreshold )
{
// Covering済みConditionセット
this.C = S;
// MatchSetにない行動をランダムに選ぶ
this.A = Actions[Configuration.MT.Next( Actions.Count - 1 )];
this.P = Configuration.P_I;
this.Epsilon = Configuration.Epsilon_I;
this.F = Configuration.F_I;
this.Exp = 0;
this.Ts = Configuration.T;
this.As = 1;
this.N = 1;
this.Epsilon_0 = Configuration.Epsilon_0;
this.St = 0;
this.M = 0;
this.S = 0;
this.GenerateTime = Configuration.T;
this.ExpThreshold = ExpThreshold;
this.EpsilonList = new double[Configuration.LookBackEpsilon + 1];
this.WinningRate = new double[Configuration.LookBackEpsilon];
// はじめは消されにくいように
for(int i = 0; i < this.WinningRate.Count(); i++)
{
this.WinningRate[i] = Configuration.F_I;
}
this.ConvergenceFlag = false;
}
/// <summary>
/// すべての取り得るStateを生成
/// </summary>
/// <param name="Length">Multiplexerの長さ</param>
/// <returns>すべての取り得るState</returns>
public static State[] AllState( int Length )
{
State[] All = new State[( int )Math.Pow( 2, Length )];
for( int i = 0; i < All.Length; i++ )
{
string S = Convert.ToString( i, 2 );
// 頭に足りない分0を付加
while( S.Length < Length )
{
S = "0" + S;
}
All[i] = new BinaryState( S );
}
return All;
}
// situationに合うものをPopulationから取り、足りないときはCovering
protected override void Covering( State S, Population P )
{
while( this.CList.Count == 0 )
{
// situationにあうものをPopulationから探す
this.CList = P.MatchSituation( S );
// Actionの種類
List<char> Actions = new List<char>();
int NumberOfActions = 0;
// Multiplexer(2進数)
if( Configuration.Type == "Binary" )
{
Actions.Add( '0' );
Actions.Add( '1' );
foreach( Classifier C in this.CList )
{
Actions.Remove( C.A );
}
NumberOfActions = 2 - Actions.Count;
}
// MatchSetにある行動が少ないとき
if( NumberOfActions < Configuration.Theta_mna )
{
// 一部を変化させたCondition
State state = new BinaryState( S );
state.Covering();
Classifier CC;
if( Configuration.ASName == "CS" || Configuration.ASName == "MaxCS" || Configuration.ASName == "Max" || Configuration.ASName == "Updatee0CS" )
{
CC = new SigmaNormalClassifier( state, Actions, Configuration.ExpThreshold );
}
else
{
CC = new NormalClassifier( state, Actions );
}
P.Add( CC );
// 整理
P.Delete();
this.CList = new List<Classifier>();
}
}
}
// Covering
public NormalClassifier( State S, List<char> Actions )
{
// Covering済みConditionセット
this.C = S;
// MatchSetにない行動をランダムに選ぶ
this.A = Actions[Configuration.MT.Next( Actions.Count - 1 )];
this.P = Configuration.P_I;
this.Epsilon = Configuration.Epsilon_I;
this.F = Configuration.F_I;
this.Exp = 0;
this.Ts = Configuration.T;
this.As = 1;
this.N = 1;
this.Epsilon_0 = Configuration.Epsilon_0;
this.St = 0;
this.M = 0;
this.S = 0;
this.GenerateTime = Configuration.T;
}
public NormalClassifier(State S)
{
// Covering済みConditionセット
this.C = S;
//action がない問題
//this.A = Actions[Configuration.MT.Next(Actions.Count - 1)];
this.P = Configuration.P_I;
this.Epsilon = Configuration.Epsilon_I;
this.F = Configuration.F_I;
this.Exp = 0;
this.Ts = Configuration.T;
this.As = 1;
this.N = 1;
this.Epsilon_0 = Configuration.Epsilon_0;
this.St = 0;
this.M = 0;
this.S = 0;
this.GenerateTime = Configuration.T;
}
/// <summary> /// GA /// </summary> /// <param name="Situation">situation</param> /// <param name="P">Population</param> public abstract void RunGA( State Situation, Population P );
// situationに合うものをPopulationから取り、足りないときはCovering
protected override void Covering( State S, Population P )
{
}
public NotCoveringMatchSet( State S, Population P )
{
this.CList = new List<Classifier>();
this.CList = P.MatchSituation( S );
}
public override void Mutation( State S )
{
int i = 0;
string state = "";
do
{
if( Configuration.MT.NextDouble() < Configuration.Myu )
{
// #とstateの切り替え
if( this.C.state[i] == '#' )
{
state += S.state[i];
}
else
{
state += '#';
}
}
else
{
state += this.C.state[i];
}
i++;
} while( i < this.C.state.Length );
this.C.state = state;
this.C.CountSharp();
if( Configuration.MT.NextDouble() < Configuration.Myu )
{
if( Configuration.Type == "Binary" )
{
this.A = ( this.A == '0' ) ? '1' : '0';
}
}
}
//// #の数 //abstract public int NumberOfSharp(); /// <summary> /// i番目の要素を自分とSで入れ替える /// </summary> /// <param name="S">入れ替え対象</param> /// <param name="i">入れ替える場所</param> public abstract void Switch( State S, int i );
//public override int NumberOfSharp()
//{
// int n = 0;
// for(int i = 0; i < this.state.Length; i++)
// {
// if(this.state[i] == '#')
// {
// n++;
// }
// }
// return n;
//}
// i番目を入れ替え
public override void Switch( State S, int i )
{
string t = "";
string s = "";
for( int j = 0; j < this.Length; j++ )
{
if( i == j )
{
t += S.state[j];
s += this.state[j];
}
else
{
t += this.state[j];
s += S.state[j];
}
}
this.state = t;
S.state = s;
this.CountSharp();
S.CountSharp();
}
/// <summary> /// situationに一致するClassifierをMatchSetに渡す /// </summary> /// <param name="S">situation</param> /// <returns>situationに一致するClassifierの集合</returns> public abstract List<Classifier> MatchSituation( State S );
/// <summary> /// situationに基づく突然変異 /// </summary> /// <param name="S">situation</param> public abstract void Mutation( State S );
public override void RunGA( State Situation, Population P )
{
double NumerositySum = 0.0;
double TimeStampSum = 0.0;
foreach( Classifier C in this.CList )
{
NumerositySum += C.N;
TimeStampSum += C.Ts * C.N;
}
if( Configuration.T - TimeStampSum / NumerositySum > Configuration.Theta_GA )
{
foreach( Classifier C in this.CList )
{
C.Ts = Configuration.T;
}
Classifier Parent_1 = this.SelectOffspring();
Classifier Parent_2 = this.SelectOffspring();
Classifier Child_1 = new NormalClassifier( ( NormalClassifier )Parent_1 );
Classifier Child_2 = new NormalClassifier( ( NormalClassifier )Parent_2 );
Child_1.N = Child_2.N = 1;
Child_1.Exp = Child_2.Exp = 0;
Child_1.St = Child_2.St = 0;
Child_1.M = Child_2.M = 0;
Child_1.S = Child_2.S = 0;
if( Configuration.MT.NextDouble() < Configuration.Chai )
{
Child_1.Crossover( Child_2 );
Child_1.P = ( Parent_1.P + Parent_2.P ) / 2;
Child_1.Epsilon = ( Parent_1.Epsilon + Parent_2.Epsilon ) / 2;
Child_1.F = ( Parent_1.F + Parent_2.F ) / 2;
//Child_1.M = ( Parent_1.M + Parent_2.M ) / 2;
//Child_1.S = ( Parent_1.S + Parent_2.S ) / 2;
Child_2.P = Child_1.P;
Child_2.Epsilon = Child_1.Epsilon;
Child_2.F = Child_1.F;
//Child_2.M = Child_1.M;
//Child_2.S = Child_1.S;
}
Child_1.F *= 0.1;
Child_2.F *= 0.1;
// bothChild
Child_1.Mutation( Situation );
Child_2.Mutation( Situation );
if( Configuration.DoGASubsumption )
{
if( Parent_1.DoesSubsume( Child_1 ) )
{
Parent_1.N++;
}
else if( Parent_2.DoesSubsume( Child_1 ) )
{
Parent_2.N++;
}
else
{
P.Insert( Child_1 );
}
P.Delete();
if( Parent_1.DoesSubsume( Child_2 ) )
{
Parent_1.N++;
}
else if( Parent_2.DoesSubsume( Child_2 ) )
{
Parent_2.N++;
}
else
{
P.Insert( Child_2 );
}
P.Delete();
}
else
{
P.Insert( Child_1 );
P.Delete();
P.Insert( Child_2 );
P.Delete();
}
}
}
// Situationに合ったClassifierをMatchSetに渡す
public override List<Classifier> MatchSituation( State S )
{
List<Classifier> MatchSet = new List<Classifier>();
foreach( Classifier C in this.CList )
{
if( S.Match( C.C ) )
{
MatchSet.Add( C );
}
}
return MatchSet;
}
public override void RunGA( State Situation, Population P )
{
double NumerositySum = 0.0;
double TimeStampSum = 0.0;
foreach( Classifier C in this.CList )
{
NumerositySum += C.N;
TimeStampSum += C.Ts * C.N;
}
if( Configuration.T - TimeStampSum / NumerositySum > Configuration.Theta_GA )
{
foreach( Classifier C in this.CList )
{
C.Ts = Configuration.T;
}
Classifier Parent_1 = this.SelectOffspring();
Classifier Parent_2 = this.SelectOffspring();
Classifier Child_1 = new SigmaNormalClassifier( ( SigmaNormalClassifier )Parent_1 );
Classifier Child_2 = new SigmaNormalClassifier( ( SigmaNormalClassifier )Parent_2 );
///nakata added
Child_1.F /= Child_1.N;
Child_2.F /= Child_2.N;
//////////////
Child_1.N = Child_2.N = 1;
Child_1.Exp = Child_2.Exp = 0;
Child_1.St = Child_2.St = 0;
Child_1.M = Child_2.M = 0;
Child_1.S = Child_2.S = 0;
//Child_1.Epsilon_0 = Child_2.Epsilon_0 = Configuration.Epsilon_0;
SigmaNormalClassifier SNP1 = ( SigmaNormalClassifier )Parent_1;
SigmaNormalClassifier SNP2 = ( SigmaNormalClassifier )Parent_2;
SigmaNormalClassifier SNC1 = ( SigmaNormalClassifier )Child_1;
SigmaNormalClassifier SNC2 = ( SigmaNormalClassifier )Child_2;
// 交叉
if( Configuration.MT.NextDouble() < Configuration.Chai )
{
Child_1.Crossover( Child_2 );
Child_1.P = ( Parent_1.P + Parent_2.P ) / 2;
//Child_1.Epsilon = Parent_1.Epsilon + Parent_2.Epsilon;
Child_1.Epsilon = ( Parent_1.Epsilon + Parent_2.Epsilon ) / 2;
Child_1.F = ( Parent_1.F + Parent_2.F ) / 2;
//Child_1.M = ( Parent_1.M + Parent_2.M ) / 2;
//Child_1.S = ( Parent_1.S + Parent_2.S ) / 2;
Child_1.Epsilon_0 = ( Parent_1.Epsilon_0 + Parent_2.Epsilon_0 ) / 2;
//Child_1.Epsilon_0 = Math.Min(Parent_1.Epsilon_0, Parent_2.Epsilon_0);
Child_2.P = Child_1.P;
Child_2.Epsilon = Child_1.Epsilon;
Child_2.F = Child_1.F;
//Child_2.M = Child_1.M;
//Child_2.S = Child_1.S;
Child_2.Epsilon_0 = Child_1.Epsilon_0;
for( int i = 0; i < SNC1.WinningRate.Count(); i++ )
{
SNC1.WinningRate[i] = SNC2.WinningRate[i] = ( SNP1.WinningRate[i] + SNP2.WinningRate[i] ) / 2;
}
}
Child_1.F *= 0.1;
Child_2.F *= 0.1;
for( int i = 0; i < SNC1.WinningRate.Count(); i++ )
{
SNC1.WinningRate[i] *= 0.1;
SNC2.WinningRate[i] *= 0.1;
}
// bothChild
Child_1.Mutation( Situation );
Child_2.Mutation( Situation );
if( Configuration.DoGASubsumption )
{
if( Parent_1.DoesSubsume( Child_1 ) )
{
Parent_1.N++;
}
else if( Parent_2.DoesSubsume( Child_1 ) )
{
Parent_2.N++;
}
else
{
P.Insert( Child_1 );
}
P.Delete();
if( Parent_1.DoesSubsume( Child_2 ) )
{
Parent_1.N++;
}
else if( Parent_2.DoesSubsume( Child_2 ) )
{
Parent_2.N++;
}
else
{
P.Insert( Child_2 );
}
P.Delete();
}
else
{
P.Insert( Child_1 );
P.Delete();
P.Insert( Child_2 );
P.Delete();
}
}
}
public NormalMatchSet( State S, Population P )
{
this.CList = new List<Classifier>();
this.Covering( S, P );
}
// コピーコンストラクタ
public BinaryState( State S )
{
this.state = S.state;
this.Length = S.Length;
this.NumberOfSharp = S.NumberOfSharp;
}
/// <summary> /// 足りないときのCovering /// </summary> /// <param name="S">situation</param> /// <param name="P">Population</param> protected abstract void Covering( State S, Population P );
/// <summary>
/// Stateに一致するか(#を考慮)
/// </summary>
/// <param name="S">比較対象</param>
/// <returns>一致(true)</returns>
public bool Match( State S )
{
if( this.state.Length != S.state.Length )
{
return false;
}
for( int i = 0; i < this.state.Length; i++ )
{
if( ( this.state[i] != S.state[i] ) && ( this.state[i] != '#' ) && ( S.state[i] != '#' ) )
{
return false;
}
}
return true;
}